Compared to PATE-GAN and other work which involves conditional GANs such as [1]} and [2]}, our work has the added constraint that for our synthetic dataset we would like to create not just synthetic samples but also synthetic classes (our identities). This issue has been explored by [3]} who propose Semantically Decomposed GANs (SD-GANs) which encourage the disentanglement of the latent space of GANs. Their GAN is trained using a latent code \(z\)  decomposed into \(z_1\)  and \(z_2\) . Two images are then produced using a fixed \(z_1\)  and varying \(z_2\) . A discriminator is then tasked with differentiating between the pair of fake images and a pair of images from the real dataset belonging to a single identity. Using a facial verification tool, the authors show that their method is successful in producing images belonging to the same identity, however in contrast with our work there is no investigation of its performance in the task of training a face recognition algorithm.
